Bing Forecasts Top Picks for the Draft

Last week we rolled out Bing Predicts for the Brazil World Cup, giving you a quick and fun way to see our predictions on who might win each match-up. This week, we’re extending our sports prediction experiment to the so-called lottery selections (top 14 picks) in the National Basketball Association’s (NBA) June amateur draft, an event which determines where many of the top amateur players will begin their careers.

How does it work?

To see the top picks, simply enter “NBA Draft Predictions.” You can also add “predictions” to a query for a specific player to see where we have him placed in the lottery (e.g. Dante Exum Predictions). Such a query also triggers the carousel, which shows all predicted lottery picks, so you can browse each of your favorite players to see where they are projected.

How do we come up with the numbers?

Harnessing the wealth of signals available to Bing and training the model on results from past seasons, we estimate the relative potential of amateur players in this year’s draft. We take publicly available data such as player statistics, player profile information, team needs, combine statistics, and expert evaluations to generate a projection of a basketball player’s on-court capabilities. We then add search data and social signals into the model to determine the projected associations between teams and players and player trends (e.g. Noah Vonleh has been trending upwards since the combines) to reach the final ordering.

A note on outcomes

Unlike the voting shows where we had high confidence in the predictions and final rank order because the outcomes depended heavily on popularity and were determined incrementally one night a week over one to two months, the NBA draft picks are made by team management and happen over the course of a few hours, where selections are made in minutes and trades happen in real-time. As a result, reaching 100% accuracy for each of the top 14 picks, while ideal, is realistically less likely. For the cases where we don’t get the exact position, our goal is to be as close as possible to the exact position. We think our model will compare favorably in relation to other expert picks.